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Personalized Depression Treatment

For many people gripped by depression, traditional therapy and medication isn't effective. A customized treatment could be the answer.

Cue is an intervention platform that transforms sensor data collected from smartphones into personalized micro-interventions to improve mental health. We analyzed the most effective-fit personal ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that deterministically change mood over time.

Predictors of Mood

Depression is one of the most prevalent causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve outcomes, clinicians must be able to identify and how treat anxiety and depression patients who are the most likely to respond to certain treatments.

The ability to tailor depression treatments is one way to do this. Utilizing mobile phone sensors as well as an artificial intelligence voice assistant and other digital tools researchers at the University of Illinois Chicago (UIC) are developing new methods to determine which patients will benefit from which treatments. Two grants totaling more than $10 million will be used to discover biological and behavior predictors of response.

So far, the majority of research into predictors of depression treatment effectiveness has centered on sociodemographic and clinical characteristics. These include demographics like gender, age and education as well as clinical characteristics such as symptom severity and comorbidities, as well as biological markers.

While many of these factors can be predicted by the information available in medical records, few studies have employed longitudinal data to explore predictors of mood in individuals. Many studies do not take into consideration the fact that mood varies significantly between individuals. Therefore, it is crucial to develop methods which allow for the analysis and measurement of personal differences between mood predictors, treatment effects, etc.

The team's new approach uses daily, in-person evaluations of mood and lifestyle variables using a smartphone app called AWARE, a cognitive evaluation with the BiAffect app and electroencephalography -- an imaging technique that monitors brain activity. This allows the team to develop algorithms that can detect distinct patterns of behavior and emotion that are different between people.

The team also created a machine learning algorithm to create dynamic predictors for each person's depression mood. The algorithm blends the individual differences to create an individual "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores which is a psychometrically validated symptom severity scale. However the correlation was not strong (Pearson's r = 0.08, the BH-adjusted p-value was 3.55 1003) and varied widely across individuals.

Predictors of Symptoms

Depression is among the leading causes of disability1 but is often not properly diagnosed and treated. In addition an absence of effective interventions and stigmatization associated with depressive disorders prevent many individuals from seeking help.

To help with personalized treatment, it is crucial to identify the factors that predict symptoms. However, current prediction methods depend on the clinical interview which has poor reliability and only detects a limited variety of characteristics that are associated with depression.2

Machine learning can be used to integrate continuous digital behavioral phenotypes that are captured by smartphone sensors and an online tracker of mental health (the Computerized Adaptive Testing Depression Inventory CAT-DI) along with other indicators of severity of symptoms has the potential to improve diagnostic accuracy and increase the effectiveness of treatment for depression. These digital phenotypes allow continuous, high-resolution measurements. They also capture a wide variety of distinct behaviors and patterns that are difficult to document using interviews.

The study enrolled University of California Los Angeles (UCLA) students experiencing mild to severe depression treatment nice symptoms. participating in the Screening and Treatment for Anxiety and Depression (STAND) program29 that was created under the UCLA Depression Grand Challenge. Participants were referred to online assistance or in-person clinics in accordance with their severity of depression. Patients who scored high on the CAT-DI scale of 35 65 were assigned online support via an instructor and those with scores of 75 patients were referred to psychotherapy in-person.

Participants were asked a series of questions at the beginning of the study about their psychosocial and demographic characteristics as well as their socioeconomic status. These included age, sex and education, as well as work and financial situation; whether they were partnered, divorced or single; their current suicidal thoughts, intentions, or attempts; and the frequency with the frequency they consumed alcohol. The CAT-DI was used for assessing the severity of antenatal depression treatment symptoms on a scale from 0-100. The CAT-DI tests were conducted every week for those that received online support, and every week for those who received in-person care.

Predictors of Treatment Reaction

A customized treatment for depression is currently a major research area, and many studies aim at identifying predictors that will enable clinicians to determine the most effective drugs for each individual. Particularly, pharmacogenetics can identify genetic variations that affect the way that the body processes antidepressants. This allows doctors to select the medications that are most likely to be most effective for each patient, reducing the time and effort in trials and errors, while eliminating any side effects that could otherwise hinder progress.

Another promising method is to construct models for prediction using multiple data sources, including clinical information and neural imaging data. These models can then be used to identify the most effective combination of variables that is predictors of a specific outcome, such as whether or not a medication is likely to improve the mood and symptoms. These models can be used to determine the patient's response to a treatment for depression and anxiety, allowing doctors maximize the effectiveness.

A new generation of machines employs machine learning techniques like supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects from multiple variables and improve predictive accuracy. These models have been proven to be useful for predicting treatment outcomes such as the response to antidepressants. These techniques are becoming increasingly popular in psychiatry, and are likely to be the norm in future treatment.

In addition to the ML-based prediction models research into the mechanisms behind depression continues. Recent findings suggest that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that the treatment for depression during pregnancy treatment will be individualized focused on therapies that target these circuits in order to restore normal functioning.

Internet-based-based therapies can be an effective method to achieve this. They can offer more customized and personalized experience for patients. For instance, one study found that a web-based program was more effective than standard treatment in alleviating symptoms and ensuring an improved quality of life for those with MDD. Additionally, a randomized controlled trial of a personalized treatment for depression demonstrated steady improvement and decreased adverse effects in a large proportion of participants.

Predictors of Side Effects

In the treatment of depression the biggest challenge is predicting and determining which antidepressant medication will have no or minimal negative side negative effects. Many patients are prescribed a variety drugs before they find a drug that is safe and effective. Pharmacogenetics offers a new and exciting method of selecting antidepressant medicines that are more effective and specific.

There are several variables that can be used to determine the antidepressant to be prescribed, such as gene variations, phenotypes of the patient such as ethnicity or gender and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, controlled trials that are randomized with larger sample sizes will be required. This is because it may be more difficult to identify moderators or interactions in trials that contain only one episode per person instead of multiple episodes spread over a long period of time.

Furthermore the prediction of a patient's response will likely require information on the severity of symptoms, comorbidities and the patient's personal perception of the effectiveness and tolerability. At present, only a handful of easily measurable sociodemographic variables as well as clinical variables are consistently associated with response to MDD. These include age, gender and race/ethnicity as well as BMI, SES and the presence of alexithymia.

The application of pharmacogenetics to treatment for depression is in its beginning stages, and many challenges remain. First, it is essential to have a clear understanding and definition of the genetic mechanisms that cause depression, and an understanding of a reliable indicator of the response to treatment. In addition, ethical issues, such as privacy and the appropriate use of personal genetic information, must be considered carefully. Pharmacogenetics can, in the long run reduce stigma associated with mental health treatments and improve the quality of treatment. But, like any approach to psychiatry careful consideration and planning is required. For now, the best course of action is to provide patients with various effective depression medications and encourage them to speak with their physicians about their concerns and experiences.